30 search results - page 5 / 6 » An Algorithmic Theory of Learning: Robust Concepts and Rando... |
SBIA
2004
Springer
13 years 11 months ago
2004
Springer
Abstract. Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
MIR
2004
ACM
13 years 11 months ago
2004
ACM
In content-based image retrieval, relevance feedback has been introduced to narrow the gap between low-level image feature and high-level semantic concept. Furthermore, to speed u...
KDD
2008
ACM
14 years 6 months ago
2008
ACM
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
ECML
2007
Springer
13 years 12 months ago
2007
Springer
The stability of sample based algorithms is a concept commonly used for parameter tuning and validity assessment. In this paper we focus on two well studied algorithms, LSI and PCA...
ECCV
2008
Springer
14 years 7 months ago
2008
Springer
Abstract. Compressive sensing (CS) is an emerging field that provides a framework for image recovery using sub-Nyquist sampling rates. The CS theory shows that a signal can be reco...